Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations980
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory130.3 KiB
Average record size in memory136.1 B

Variable types

Text3
Categorical2
Numeric12

Alerts

Acousticness is highly overall correlated with EnergyHigh correlation
Danceability is highly overall correlated with ValenceHigh correlation
Energy is highly overall correlated with Acousticness and 1 other fieldsHigh correlation
Loudness is highly overall correlated with EnergyHigh correlation
Valence is highly overall correlated with DanceabilityHigh correlation
Time_Signature is highly imbalanced (82.7%)Imbalance
Key has 140 (14.3%) zerosZeros
Instrumentalness has 285 (29.1%) zerosZeros
Popularity has 15 (1.5%) zerosZeros

Reproduction

Analysis started2024-09-08 13:49:14.511415
Analysis finished2024-09-08 13:49:31.384825
Duration16.87 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Track
Text

Distinct965
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:31.924096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length66
Median length47
Mean length18.290816
Min length2

Characters and Unicode

Total characters17925
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique950 ?
Unique (%)96.9%

Sample

1st rowAbc
2nd rowLet It Be
3rd rowI Want You Back
4th rowCecilia
5th rowSpirit In The Sky
ValueCountFrequency (%)
the 161
 
4.5%
you 127
 
3.5%
love 115
 
3.2%
to 81
 
2.2%
me 75
 
2.1%
i 70
 
1.9%
in 63
 
1.7%
a 52
 
1.4%
my 48
 
1.3%
of 44
 
1.2%
Other values (1136) 2778
76.9%
2024-09-08T10:49:32.388072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2634
 
14.7%
e 1697
 
9.5%
o 1274
 
7.1%
n 973
 
5.4%
a 838
 
4.7%
i 739
 
4.1%
t 694
 
3.9%
r 591
 
3.3%
h 520
 
2.9%
l 497
 
2.8%
Other values (69) 7468
41.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2634
 
14.7%
e 1697
 
9.5%
o 1274
 
7.1%
n 973
 
5.4%
a 838
 
4.7%
i 739
 
4.1%
t 694
 
3.9%
r 591
 
3.3%
h 520
 
2.9%
l 497
 
2.8%
Other values (69) 7468
41.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2634
 
14.7%
e 1697
 
9.5%
o 1274
 
7.1%
n 973
 
5.4%
a 838
 
4.7%
i 739
 
4.1%
t 694
 
3.9%
r 591
 
3.3%
h 520
 
2.9%
l 497
 
2.8%
Other values (69) 7468
41.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2634
 
14.7%
e 1697
 
9.5%
o 1274
 
7.1%
n 973
 
5.4%
a 838
 
4.7%
i 739
 
4.1%
t 694
 
3.9%
r 591
 
3.3%
h 520
 
2.9%
l 497
 
2.8%
Other values (69) 7468
41.7%

Artist
Text

Distinct530
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:32.660643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length51
Median length36
Mean length13.591837
Min length1

Characters and Unicode

Total characters13320
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique349 ?
Unique (%)35.6%

Sample

1st rowThe Jackson 5
2nd rowThe Beatles
3rd rowThe Jackson 5
4th rowSimon & Garfunkel
5th rowNorman Greenbaum
ValueCountFrequency (%)
the 169
 
7.3%
97
 
4.2%
john 38
 
1.7%
band 33
 
1.4%
and 27
 
1.2%
paul 25
 
1.1%
elton 16
 
0.7%
barry 15
 
0.7%
simon 15
 
0.7%
jackson 14
 
0.6%
Other values (790) 1854
80.5%
2024-09-08T10:49:33.061789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1323
 
9.9%
e 1304
 
9.8%
a 930
 
7.0%
n 917
 
6.9%
r 791
 
5.9%
o 777
 
5.8%
i 726
 
5.5%
t 580
 
4.4%
l 559
 
4.2%
s 513
 
3.9%
Other values (57) 4900
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13320
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1323
 
9.9%
e 1304
 
9.8%
a 930
 
7.0%
n 917
 
6.9%
r 791
 
5.9%
o 777
 
5.8%
i 726
 
5.5%
t 580
 
4.4%
l 559
 
4.2%
s 513
 
3.9%
Other values (57) 4900
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13320
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1323
 
9.9%
e 1304
 
9.8%
a 930
 
7.0%
n 917
 
6.9%
r 791
 
5.9%
o 777
 
5.8%
i 726
 
5.5%
t 580
 
4.4%
l 559
 
4.2%
s 513
 
3.9%
Other values (57) 4900
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13320
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1323
 
9.9%
e 1304
 
9.8%
a 930
 
7.0%
n 917
 
6.9%
r 791
 
5.9%
o 777
 
5.8%
i 726
 
5.5%
t 580
 
4.4%
l 559
 
4.2%
s 513
 
3.9%
Other values (57) 4900
36.8%
Distinct250
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:33.413881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0040816
Min length4

Characters and Unicode

Total characters3924
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)9.1%

Sample

1st row2:42
2nd row4:03
3rd row2:56
4th row2:54
5th row4:02
ValueCountFrequency (%)
3:27 17
 
1.7%
2:54 13
 
1.3%
3:32 13
 
1.3%
3:53 13
 
1.3%
3:35 12
 
1.2%
3:33 12
 
1.2%
3:28 12
 
1.2%
3:23 11
 
1.1%
3:47 11
 
1.1%
3:50 10
 
1.0%
Other values (240) 856
87.3%
2024-09-08T10:49:33.966345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 980
25.0%
3 731
18.6%
4 492
12.5%
2 450
11.5%
5 317
 
8.1%
1 292
 
7.4%
0 239
 
6.1%
7 129
 
3.3%
6 106
 
2.7%
8 103
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3924
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 980
25.0%
3 731
18.6%
4 492
12.5%
2 450
11.5%
5 317
 
8.1%
1 292
 
7.4%
0 239
 
6.1%
7 129
 
3.3%
6 106
 
2.7%
8 103
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3924
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 980
25.0%
3 731
18.6%
4 492
12.5%
2 450
11.5%
5 317
 
8.1%
1 292
 
7.4%
0 239
 
6.1%
7 129
 
3.3%
6 106
 
2.7%
8 103
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3924
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 980
25.0%
3 731
18.6%
4 492
12.5%
2 450
11.5%
5 317
 
8.1%
1 292
 
7.4%
0 239
 
6.1%
7 129
 
3.3%
6 106
 
2.7%
8 103
 
2.6%

Time_Signature
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
4
924 
3
 
51
1
 
3
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters980
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 924
94.3%
3 51
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Length

2024-09-08T10:49:34.076533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-08T10:49:34.171054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 924
94.3%
3 51
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
4 924
94.3%
3 51
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 924
94.3%
3 51
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 924
94.3%
3 51
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 924
94.3%
3 51
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct494
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58769306
Minimum0.0942
Maximum0.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:34.275373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0942
5-th percentile0.30195
Q10.486
median0.6
Q30.698
95-th percentile0.82405
Maximum0.985
Range0.8908
Interquartile range (IQR)0.212

Descriptive statistics

Standard deviation0.15785557
Coefficient of variation (CV)0.26860207
Kurtosis-0.29201425
Mean0.58769306
Median Absolute Deviation (MAD)0.103
Skewness-0.33757863
Sum575.9392
Variance0.024918381
MonotonicityNot monotonic
2024-09-08T10:49:34.407860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.637 9
 
0.9%
0.68 7
 
0.7%
0.665 7
 
0.7%
0.649 6
 
0.6%
0.639 6
 
0.6%
0.671 6
 
0.6%
0.541 6
 
0.6%
0.565 6
 
0.6%
0.669 6
 
0.6%
0.722 5
 
0.5%
Other values (484) 916
93.5%
ValueCountFrequency (%)
0.0942 1
0.1%
0.149 2
0.2%
0.16 1
0.1%
0.164 1
0.1%
0.185 1
0.1%
0.195 1
0.1%
0.203 1
0.1%
0.205 1
0.1%
0.207 1
0.1%
0.212 1
0.1%
ValueCountFrequency (%)
0.985 1
 
0.1%
0.965 1
 
0.1%
0.946 1
 
0.1%
0.925 1
 
0.1%
0.919 1
 
0.1%
0.912 3
0.3%
0.911 2
0.2%
0.908 1
 
0.1%
0.9 1
 
0.1%
0.889 1
 
0.1%

Energy
Real number (ℝ)

HIGH CORRELATION 

Distinct543
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58087655
Minimum0.00532
Maximum0.995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:34.532701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00532
5-th percentile0.2489
Q10.428
median0.583
Q30.73125
95-th percentile0.90805
Maximum0.995
Range0.98968
Interquartile range (IQR)0.30325

Descriptive statistics

Standard deviation0.20237928
Coefficient of variation (CV)0.34840325
Kurtosis-0.58197862
Mean0.58087655
Median Absolute Deviation (MAD)0.1515
Skewness-0.1399714
Sum569.25902
Variance0.040957373
MonotonicityNot monotonic
2024-09-08T10:49:34.658604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.673 7
 
0.7%
0.528 7
 
0.7%
0.641 6
 
0.6%
0.644 6
 
0.6%
0.532 5
 
0.5%
0.409 5
 
0.5%
0.56 5
 
0.5%
0.634 5
 
0.5%
0.511 4
 
0.4%
0.573 4
 
0.4%
Other values (533) 926
94.5%
ValueCountFrequency (%)
0.00532 1
0.1%
0.0088 1
0.1%
0.0264 1
0.1%
0.0265 1
0.1%
0.0751 1
0.1%
0.0803 1
0.1%
0.0809 1
0.1%
0.0897 1
0.1%
0.112 1
0.1%
0.116 1
0.1%
ValueCountFrequency (%)
0.995 2
0.2%
0.989 1
0.1%
0.987 1
0.1%
0.98 1
0.1%
0.979 1
0.1%
0.974 1
0.1%
0.969 1
0.1%
0.968 2
0.2%
0.961 1
0.1%
0.957 1
0.1%

Key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1918367
Minimum0
Maximum11
Zeros140
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:34.773428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38.25
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation3.5778136
Coefficient of variation (CV)0.68912291
Kurtosis-1.2974935
Mean5.1918367
Median Absolute Deviation (MAD)3
Skewness-0.012988478
Sum5088
Variance12.80075
MonotonicityNot monotonic
2024-09-08T10:49:34.876961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 140
14.3%
7 121
12.3%
9 116
11.8%
2 101
10.3%
5 95
9.7%
4 81
8.3%
1 74
7.6%
11 65
6.6%
10 64
6.5%
8 52
 
5.3%
Other values (2) 71
7.2%
ValueCountFrequency (%)
0 140
14.3%
1 74
7.6%
2 101
10.3%
3 25
 
2.6%
4 81
8.3%
5 95
9.7%
6 46
 
4.7%
7 121
12.3%
8 52
 
5.3%
9 116
11.8%
ValueCountFrequency (%)
11 65
6.6%
10 64
6.5%
9 116
11.8%
8 52
5.3%
7 121
12.3%
6 46
 
4.7%
5 95
9.7%
4 81
8.3%
3 25
 
2.6%
2 101
10.3%

Loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct916
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.8638735
Minimum-31.646
Maximum-2.34
Zeros0
Zeros (%)0.0%
Negative980
Negative (%)100.0%
Memory size7.8 KiB
2024-09-08T10:49:35.007915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-31.646
5-th percentile-15.7293
Q1-12.3585
median-9.5585
Q3-7.0955
95-th percentile-4.68055
Maximum-2.34
Range29.306
Interquartile range (IQR)5.263

Descriptive statistics

Standard deviation3.7184168
Coefficient of variation (CV)-0.37697328
Kurtosis2.6726028
Mean-9.8638735
Median Absolute Deviation (MAD)2.601
Skewness-0.94434879
Sum-9666.596
Variance13.826623
MonotonicityNot monotonic
2024-09-08T10:49:35.157995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12.472 3
 
0.3%
-4.653 2
 
0.2%
-12.264 2
 
0.2%
-7.246 2
 
0.2%
-12.923 2
 
0.2%
-10.36 2
 
0.2%
-8.503 2
 
0.2%
-10.742 2
 
0.2%
-9.15 2
 
0.2%
-8.442 2
 
0.2%
Other values (906) 959
97.9%
ValueCountFrequency (%)
-31.646 1
0.1%
-30 1
0.1%
-27.103 1
0.1%
-27.09 1
0.1%
-26.128 1
0.1%
-23.56 1
0.1%
-21.657 1
0.1%
-21.644 1
0.1%
-20.518 1
0.1%
-20.439 1
0.1%
ValueCountFrequency (%)
-2.34 1
0.1%
-2.515 1
0.1%
-2.588 1
0.1%
-2.621 1
0.1%
-2.785 1
0.1%
-3.081 1
0.1%
-3.144 1
0.1%
-3.222 1
0.1%
-3.226 1
0.1%
-3.471 1
0.1%

Mode
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
1
745 
0
235 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters980
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 745
76.0%
0 235
 
24.0%

Length

2024-09-08T10:49:35.287406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-08T10:49:35.368057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 745
76.0%
0 235
 
24.0%

Most occurring characters

ValueCountFrequency (%)
1 745
76.0%
0 235
 
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 745
76.0%
0 235
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 745
76.0%
0 235
 
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 745
76.0%
0 235
 
24.0%

Speechiness
Real number (ℝ)

Distinct455
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.059923265
Minimum0.0232
Maximum0.737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:35.493581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0232
5-th percentile0.026795
Q10.0313
median0.0383
Q30.056725
95-th percentile0.18005
Maximum0.737
Range0.7138
Interquartile range (IQR)0.025425

Descriptive statistics

Standard deviation0.065534544
Coefficient of variation (CV)1.0936411
Kurtosis25.565233
Mean0.059923265
Median Absolute Deviation (MAD)0.0094
Skewness4.4037247
Sum58.7248
Variance0.0042947765
MonotonicityNot monotonic
2024-09-08T10:49:35.627857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0336 9
 
0.9%
0.0346 9
 
0.9%
0.0341 8
 
0.8%
0.0283 8
 
0.8%
0.0287 8
 
0.8%
0.0302 8
 
0.8%
0.0325 8
 
0.8%
0.0282 7
 
0.7%
0.0369 7
 
0.7%
0.0335 7
 
0.7%
Other values (445) 901
91.9%
ValueCountFrequency (%)
0.0232 1
 
0.1%
0.0239 1
 
0.1%
0.024 2
0.2%
0.0241 1
 
0.1%
0.0243 2
0.2%
0.0245 1
 
0.1%
0.0246 2
0.2%
0.0247 1
 
0.1%
0.0248 4
0.4%
0.0249 3
0.3%
ValueCountFrequency (%)
0.737 1
0.1%
0.576 1
0.1%
0.467 1
0.1%
0.457 1
0.1%
0.452 1
0.1%
0.448 1
0.1%
0.405 2
0.2%
0.368 1
0.1%
0.364 1
0.1%
0.361 1
0.1%

Acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct718
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33295901
Minimum2.23 × 10-5
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:35.782780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.23 × 10-5
5-th percentile0.005728
Q10.07655
median0.2705
Q30.54425
95-th percentile0.8572
Maximum0.996
Range0.9959777
Interquartile range (IQR)0.4677

Descriptive statistics

Standard deviation0.28007633
Coefficient of variation (CV)0.8411736
Kurtosis-0.84456665
Mean0.33295901
Median Absolute Deviation (MAD)0.2149
Skewness0.5928114
Sum326.29983
Variance0.078442752
MonotonicityNot monotonic
2024-09-08T10:49:35.938125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.357 8
 
0.8%
0.305 5
 
0.5%
0.484 5
 
0.5%
0.309 5
 
0.5%
0.181 5
 
0.5%
0.499 4
 
0.4%
0.881 4
 
0.4%
0.134 4
 
0.4%
0.81 4
 
0.4%
0.22 4
 
0.4%
Other values (708) 932
95.1%
ValueCountFrequency (%)
2.23 × 10-51
0.1%
0.000109 1
0.1%
0.000133 1
0.1%
0.000215 1
0.1%
0.000261 1
0.1%
0.000274 1
0.1%
0.00028 1
0.1%
0.000288 1
0.1%
0.000385 1
0.1%
0.000598 1
0.1%
ValueCountFrequency (%)
0.996 1
0.1%
0.994 1
0.1%
0.992 1
0.1%
0.983 1
0.1%
0.973 1
0.1%
0.971 1
0.1%
0.965 1
0.1%
0.959 1
0.1%
0.953 1
0.1%
0.95 1
0.1%

Instrumentalness
Real number (ℝ)

ZEROS 

Distinct614
Distinct (%)62.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.048239716
Minimum0
Maximum0.97
Zeros285
Zeros (%)29.1%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:36.237941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.025 × 10-5
Q30.0028225
95-th percentile0.3584
Maximum0.97
Range0.97
Interquartile range (IQR)0.0028225

Descriptive statistics

Standard deviation0.16571232
Coefficient of variation (CV)3.4351844
Kurtosis16.970978
Mean0.048239716
Median Absolute Deviation (MAD)5.025 × 10-5
Skewness4.1566986
Sum47.274922
Variance0.027460573
MonotonicityNot monotonic
2024-09-08T10:49:36.373472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 285
29.1%
1.81 × 10-64
 
0.4%
0.000122 4
 
0.4%
0.00141 3
 
0.3%
0.00031 3
 
0.3%
0.000171 3
 
0.3%
0.00014 3
 
0.3%
0.0133 2
 
0.2%
0.00993 2
 
0.2%
2.15 × 10-62
 
0.2%
Other values (604) 669
68.3%
ValueCountFrequency (%)
0 285
29.1%
1 × 10-61
 
0.1%
1.08 × 10-61
 
0.1%
1.09 × 10-61
 
0.1%
1.1 × 10-62
 
0.2%
1.2 × 10-61
 
0.1%
1.22 × 10-61
 
0.1%
1.23 × 10-61
 
0.1%
1.28 × 10-61
 
0.1%
1.31 × 10-62
 
0.2%
ValueCountFrequency (%)
0.97 1
0.1%
0.968 1
0.1%
0.963 1
0.1%
0.959 2
0.2%
0.944 1
0.1%
0.94 2
0.2%
0.92 1
0.1%
0.916 2
0.2%
0.912 1
0.1%
0.909 1
0.1%

Liveness
Real number (ℝ)

Distinct531
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17626949
Minimum0.015
Maximum0.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:36.511732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.015
5-th percentile0.04789
Q10.0863
median0.119
Q30.19825
95-th percentile0.5444
Maximum0.985
Range0.97
Interquartile range (IQR)0.11195

Descriptive statistics

Standard deviation0.15586243
Coefficient of variation (CV)0.88422806
Kurtosis6.2459433
Mean0.17626949
Median Absolute Deviation (MAD)0.04365
Skewness2.3647633
Sum172.7441
Variance0.024293097
MonotonicityNot monotonic
2024-09-08T10:49:36.650951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.105 12
 
1.2%
0.108 11
 
1.1%
0.113 10
 
1.0%
0.103 10
 
1.0%
0.12 9
 
0.9%
0.114 9
 
0.9%
0.122 9
 
0.9%
0.115 9
 
0.9%
0.109 8
 
0.8%
0.132 8
 
0.8%
Other values (521) 885
90.3%
ValueCountFrequency (%)
0.015 1
0.1%
0.0166 1
0.1%
0.0188 1
0.1%
0.0199 1
0.1%
0.0295 2
0.2%
0.0309 1
0.1%
0.0318 1
0.1%
0.032 1
0.1%
0.0339 1
0.1%
0.034 1
0.1%
ValueCountFrequency (%)
0.985 1
0.1%
0.974 1
0.1%
0.962 1
0.1%
0.957 1
0.1%
0.935 1
0.1%
0.9 1
0.1%
0.892 1
0.1%
0.805 1
0.1%
0.792 1
0.1%
0.78 1
0.1%

Valence
Real number (ℝ)

HIGH CORRELATION 

Distinct570
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62114919
Minimum1 × 10-5
Maximum0.989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:36.800232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-5
5-th percentile0.17785
Q10.42175
median0.6505
Q30.845
95-th percentile0.962
Maximum0.989
Range0.98899
Interquartile range (IQR)0.42325

Descriptive statistics

Standard deviation0.251799
Coefficient of variation (CV)0.40537604
Kurtosis-0.94658567
Mean0.62114919
Median Absolute Deviation (MAD)0.2065
Skewness-0.38978714
Sum608.72621
Variance0.063402737
MonotonicityNot monotonic
2024-09-08T10:49:36.939427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.962 9
 
0.9%
0.963 8
 
0.8%
0.971 6
 
0.6%
0.969 6
 
0.6%
0.967 5
 
0.5%
0.926 5
 
0.5%
0.826 5
 
0.5%
0.718 5
 
0.5%
0.961 5
 
0.5%
0.345 5
 
0.5%
Other values (560) 921
94.0%
ValueCountFrequency (%)
1 × 10-51
0.1%
0.0346 1
0.1%
0.0348 1
0.1%
0.0385 1
0.1%
0.0393 1
0.1%
0.0397 1
0.1%
0.0558 1
0.1%
0.0579 1
0.1%
0.0589 1
0.1%
0.0685 2
0.2%
ValueCountFrequency (%)
0.989 1
 
0.1%
0.985 1
 
0.1%
0.981 1
 
0.1%
0.979 1
 
0.1%
0.978 1
 
0.1%
0.973 1
 
0.1%
0.972 1
 
0.1%
0.971 6
0.6%
0.97 2
 
0.2%
0.969 6
0.6%

Tempo
Real number (ℝ)

Distinct955
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.87277
Minimum53.986
Maximum211.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:37.127365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum53.986
5-th percentile79.2346
Q199.91975
median117.4365
Q3134.00775
95-th percentile170.91155
Maximum211.27
Range157.284
Interquartile range (IQR)34.088

Descriptive statistics

Standard deviation27.023443
Coefficient of variation (CV)0.22733081
Kurtosis0.38321675
Mean118.87277
Median Absolute Deviation (MAD)17.2085
Skewness0.58151282
Sum116495.31
Variance730.26647
MonotonicityNot monotonic
2024-09-08T10:49:37.329418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102.977 3
 
0.3%
130.166 2
 
0.2%
120.157 2
 
0.2%
85.126 2
 
0.2%
79.764 2
 
0.2%
95.048 2
 
0.2%
114.543 2
 
0.2%
103.01 2
 
0.2%
166.139 2
 
0.2%
107.383 2
 
0.2%
Other values (945) 959
97.9%
ValueCountFrequency (%)
53.986 1
0.1%
61.53 1
0.1%
62.204 1
0.1%
63.059 1
0.1%
65.09 1
0.1%
65.832 1
0.1%
65.861 1
0.1%
67.006 1
0.1%
68.482 1
0.1%
68.69 1
0.1%
ValueCountFrequency (%)
211.27 1
0.1%
207.266 1
0.1%
205.845 1
0.1%
205.747 1
0.1%
203.812 1
0.1%
202.297 1
0.1%
202.14 1
0.1%
201.467 1
0.1%
200.813 1
0.1%
200.423 1
0.1%

Popularity
Real number (ℝ)

ZEROS 

Distinct87
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.289796
Minimum0
Maximum90
Zeros15
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:37.529601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.95
Q143
median56
Q366
95-th percentile78
Maximum90
Range90
Interquartile range (IQR)23

Descriptive statistics

Standard deviation18.262967
Coefficient of variation (CV)0.34271039
Kurtosis0.55759918
Mean53.289796
Median Absolute Deviation (MAD)11
Skewness-0.81715848
Sum52224
Variance333.53595
MonotonicityNot monotonic
2024-09-08T10:49:37.705338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 31
 
3.2%
64 31
 
3.2%
55 30
 
3.1%
49 26
 
2.7%
74 26
 
2.7%
51 25
 
2.6%
71 25
 
2.6%
47 25
 
2.6%
67 25
 
2.6%
59 24
 
2.4%
Other values (77) 712
72.7%
ValueCountFrequency (%)
0 15
1.5%
1 2
 
0.2%
2 2
 
0.2%
3 1
 
0.1%
4 3
 
0.3%
5 2
 
0.2%
6 2
 
0.2%
7 5
 
0.5%
8 1
 
0.1%
9 2
 
0.2%
ValueCountFrequency (%)
90 2
 
0.2%
89 1
 
0.1%
86 3
 
0.3%
85 3
 
0.3%
84 5
0.5%
83 6
0.6%
82 4
 
0.4%
81 9
0.9%
80 10
1.0%
79 4
 
0.4%

Year
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1974.5633
Minimum1970
Maximum1979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-09-08T10:49:37.811581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1970
Q11972
median1975
Q31977
95-th percentile1979
Maximum1979
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8505644
Coefficient of variation (CV)0.001443643
Kurtosis-1.2104577
Mean1974.5633
Median Absolute Deviation (MAD)2
Skewness-0.020838957
Sum1935072
Variance8.1257176
MonotonicityIncreasing
2024-09-08T10:49:37.916131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1978 100
10.2%
1973 100
10.2%
1977 100
10.2%
1976 100
10.2%
1974 99
10.1%
1975 99
10.1%
1979 99
10.1%
1972 97
9.9%
1971 96
9.8%
1970 90
9.2%
ValueCountFrequency (%)
1970 90
9.2%
1971 96
9.8%
1972 97
9.9%
1973 100
10.2%
1974 99
10.1%
1975 99
10.1%
1976 100
10.2%
1977 100
10.2%
1978 100
10.2%
1979 99
10.1%
ValueCountFrequency (%)
1979 99
10.1%
1978 100
10.2%
1977 100
10.2%
1976 100
10.2%
1975 99
10.1%
1974 99
10.1%
1973 100
10.2%
1972 97
9.9%
1971 96
9.8%
1970 90
9.2%

Interactions

2024-09-08T10:49:29.513556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:15.426843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:16.819020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:18.029875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:19.221474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:20.429778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:22.145891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:23.255851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:24.450818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:25.586402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:27.011198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:28.342231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:29.610890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:15.536088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:16.915313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:18.141445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:19.312993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:20.595829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:22.222967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:23.343277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:24.537376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:25.672237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:27.123749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:28.442660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:29.704916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:15.634572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:17.006822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:18.242347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:19.403042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:20.747849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:22.312291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:23.435080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:24.637384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:25.924599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:27.229401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:28.532005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:29.795713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:15.724673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:17.095754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:18.335426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:19.503784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:20.886147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:22.411783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:23.538323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:24.727754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:26.017212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:27.336319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:28.621509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:29.913362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:15.819874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:17.206045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:18.441629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:19.603710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:21.025814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:22.507382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:23.636655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:24.823173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:26.146800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:27.429416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:28.712134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:30.037612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:15.932459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:17.338751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:18.555291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:19.702607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:21.311914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:22.605957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:23.747411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:24.927210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:26.268041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:27.548635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:28.804837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:30.203632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:16.026868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:17.440235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:18.650834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:19.800738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:21.472923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:22.706614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:23.843775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:25.013293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:26.382410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:27.665183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:28.907651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:30.305823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:16.226834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:17.533545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:18.749211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:19.896067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:21.614211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:22.805084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:23.948110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:25.109595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:26.474955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:27.774529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:28.995301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:30.400336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:16.335057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:17.642019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:18.841093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:20.042950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:21.726441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:22.899667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:24.052512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:25.201180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:26.600960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:27.887529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:29.081683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:30.497713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:16.464735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:17.744269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:18.941444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:20.158235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:21.837666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:22.984597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:24.159451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:25.299234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:26.701645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:27.997053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:29.202157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:30.603623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:16.602589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:17.837503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:19.041299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:20.249983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:21.972294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:23.071020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:24.275703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:25.394535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:26.806241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:28.117505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:29.325634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:30.686291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:16.719040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:17.931658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:19.131350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:20.331791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:22.064980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:23.170641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:24.355979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:25.480821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:26.906911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:28.239265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-08T10:49:29.415894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-08T10:49:38.012404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AcousticnessDanceabilityEnergyInstrumentalnessKeyLivenessLoudnessModePopularitySpeechinessTempoTime_SignatureValenceYear
Acousticness1.000-0.249-0.564-0.0330.0360.053-0.3720.138-0.111-0.189-0.1050.183-0.208-0.113
Danceability-0.2491.0000.211-0.0210.010-0.2260.0760.0850.1010.228-0.1060.1440.5340.123
Energy-0.5640.2111.000-0.000-0.0510.0580.6570.0500.0770.3320.1420.1690.3940.011
Instrumentalness-0.033-0.021-0.0001.0000.026-0.074-0.1770.1090.000-0.075-0.0530.108-0.012-0.016
Key0.0360.010-0.0510.0261.0000.067-0.0800.208-0.034-0.004-0.0160.000-0.017-0.040
Liveness0.053-0.2260.058-0.0740.0671.0000.0750.026-0.0290.031-0.0180.000-0.142-0.009
Loudness-0.3720.0760.657-0.177-0.0800.0751.0000.0120.1530.1550.0620.4130.0340.036
Mode0.1380.0850.0500.1090.2080.0260.0121.0000.0920.0430.0000.0520.0000.062
Popularity-0.1110.1010.0770.000-0.034-0.0290.1530.0921.0000.0240.0180.048-0.0220.137
Speechiness-0.1890.2280.332-0.075-0.0040.0310.1550.0430.0241.0000.1240.0000.091-0.050
Tempo-0.105-0.1060.142-0.053-0.016-0.0180.0620.0000.0180.1241.0000.1260.0680.034
Time_Signature0.1830.1440.1690.1080.0000.0000.4130.0520.0480.0000.1261.0000.1670.032
Valence-0.2080.5340.394-0.012-0.017-0.1420.0340.000-0.0220.0910.0680.1671.000-0.034
Year-0.1130.1230.011-0.016-0.040-0.0090.0360.0620.137-0.0500.0340.032-0.0341.000

Missing values

2024-09-08T10:49:31.027960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-08T10:49:31.229570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TrackArtistDurationTime_SignatureDanceabilityEnergyKeyLoudnessModeSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoPopularityYear
0AbcThe Jackson 52:4240.6820.9263-2.51500.06070.0404000.0000000.19000.860105.969811970
1Let It BeThe Beatles4:0340.4430.4030-8.33910.03220.6310000.0000000.11100.410143.462781970
2I Want You BackThe Jackson 52:5640.4690.5388-13.55910.05750.3050000.0001140.37000.885196.606781970
3CeciliaSimon & Garfunkel2:5440.7550.8760-8.86710.03620.3570000.0000050.22000.954102.762761970
4Spirit In The SkyNorman Greenbaum4:0240.6090.6179-7.09110.03070.0994000.0040400.11800.543128.903751970
5Love Grows (WHERE My Rosemary Goes)Edison Lighthouse2:5440.5680.8249-4.61310.02990.4030000.0000000.08550.753108.625731970
6The LetterJoe Cocker1:3140.7210.4968-6.29610.06450.1400000.0001010.10200.18081.499721970
7The House Of The Rising SunFrijid Pink4:3130.2950.5849-6.69600.03450.0003850.2180000.09960.228117.200711970
8Fire And RainJames Taylor3:2340.5970.2715-17.29310.03940.7660000.0119000.09330.33876.271711970
9In The SummertimeMungo Jerry3:3140.7540.4494-14.01310.06150.7240000.0000000.16200.97382.751711970
TrackArtistDurationTime_SignatureDanceabilityEnergyKeyLoudnessModeSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoPopularityYear
970In The NavyVillage People3:4540.7590.8897-10.59200.05020.125000.0000000.04100.886126.201381979
971Mama Can’t Buy You LoveElton John4:0440.5290.4325-14.24510.03330.524000.0000000.11500.55594.382361979
972Goodnight TonightPaul McCartney & Wings4:2040.7480.6831-9.88500.04660.056600.0006390.08090.943123.385351979
973We’ve Got TonightBob Seger & The Silver Bullet Band3:3540.3790.3878-9.28310.02780.757000.0000000.10300.22261.530261979
974The Main Event/FightBarbra Streisand4:5340.6470.8757-8.50310.04310.050200.0000000.17200.684137.392251979
975He’s The Greatest DancerSister Sledge6:1540.7000.8157-9.71100.04400.001150.0012400.09010.837113.245141979
976Don’t Cry Out LoudMelissa Manchester2:1540.2980.2520-8.95010.03390.901000.0000090.12700.19390.95591979
977When You’re In Love With A Beautiful WomanDr. Hook2:5440.6650.6638-11.36710.03860.485000.0068200.15700.792110.65671979
978I’ll Never Love This Way AgainDionne Warwick2:5840.4520.4348-8.87010.03990.792000.0139000.16500.247137.70251979
979Dim All The NightsDonna Summer4:0840.7580.5407-10.91110.03850.055100.0000000.03430.661121.58101979